This paper presents a second-path inference-detection approach based on association cardinalities.* It is applicable to the detection of second paths that do not involve functional dependencies or foreign keys. It provides for an analysis sieve that begins with the analysis of an object model of the database. The goal of the analysis is to detect cases in the database in which a small number of values in the target entity can be associated with a single value in the anchor entity. The number of values is called the association cardinality from anchor to target. Inference vulnerabilities occur for cases of small association cardinalities. The analysis sieve processes the data model of the database to detect cases of small association cardinality. For cases with high cardinality associations, the sieve mines the database to detect cases of small instance-level association cardinalities.
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